MYM-A MODO GRADIENTE

import tkinter as tk

from tkinter import messagebox

import MetaTrader5 as mt5

import pandas as pd

import numpy as np

import matplotlib.pyplot as plt

from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg

from scipy.signal import find_peaks

from sklearn.preprocessing import MinMaxScaler

from sklearn.ensemble import GradientBoostingRegressor

from tensorflow.keras.models import Sequential

from tensorflow.keras.layers import LSTM, Dense

def main():

    # Create the main window

    root = tk.Tk()

    root.title("MT5MYM-A")

    root.geometry("1200x800")

    # Create a frame for the login UI

    login_frame = tk.Frame(root, width=400, height=600, bg="lightgrey")

    login_frame.pack(side=tk.LEFT, fill=tk.BOTH, expand=True)

    # Title for the login UI

    login_title = tk.Label(login_frame, text="🏧MYM-A", font=("Helvetica", 20), bg="lightgrey")

    login_title.pack(pady=20)

    # Login fields

    login_label = tk.Label(login_frame, text="Login:", font=("Helvetica", 14), bg="lightgrey")

    login_label.pack(pady=5)

    login_entry = tk.Entry(login_frame, font=("Helvetica", 14))

    login_entry.pack(pady=5)

    login_entry.insert(0, "312128713")  # Pre-fill with your login

    password_label = tk.Label(login_frame, text="Password:", font=("Helvetica", 14), bg="lightgrey")

    password_label.pack(pady=5)

    password_entry = tk.Entry(login_frame, show="*", font=("Helvetica", 14))

    password_entry.pack(pady=5)

    password_entry.insert(0, "Sexo247420@")  # Pre-fill with your password

    server_label = tk.Label(login_frame, text="Server:", font=("Helvetica", 14), bg="lightgrey")

    server_label.pack(pady=5)

    server_entry = tk.Entry(login_frame, font=("Helvetica", 14))

    server_entry.pack(pady=5)

    server_entry.insert(0, "XMGlobal-MT5 7")  # Pre-fill with your server

    # Connect button

    connect_button = tk.Button(login_frame, text="Connect", font=("Helvetica", 14),

                               command=lambda: connect_to_mt5(login_entry.get(), password_entry.get(), server_entry.get(), root))

    connect_button.pack(pady=20)

    # Create a frame for the main content

    main_frame = tk.Frame(root, width=800, height=600)

    main_frame.pack(side=tk.RIGHT, fill=tk.BOTH, expand=True)

    # Create a label with the text "USD/MX"

    label = tk.Label(main_frame, text="USD/MXN", font=("Helvetica", 24))

    label.pack(expand=True)

    # Create a label with the text "Loading..." at the bottom

    loading_label = tk.Label(main_frame, text="Loading...", font=("Helvetica", 14))

    loading_label.pack(side=tk.BOTTOM, pady=10)

    # Start the Tkinter event loop

    root.mainloop()

def connect_to_mt5(login, password, server, root):

    # Initialize MetaTrader 5

    if not mt5.initialize():

        messagebox.showerror("Error", "initialize() failed")

        mt5.shutdown()

        return

    # Log in to the MetaTrader 5 account

    authorized = mt5.login(login=int(login), password=password, server=server)

    if authorized:

        print("Connected to MetaTrader 5")

        display_account_info(root)

        fetch_and_display_chart(root)

    else:

        messagebox.showerror("Error", "Failed to connect to MetaTrader 5")

def display_account_info(root):

    # Get account info

    account_info = mt5.account_info()

    if account_info is None:

        messagebox.showerror("Error", "Failed to get account info")

        return

    # Create a new window for account information

    info_window = tk.Toplevel(root)

    info_window.title("Account Information")

    info_window.geometry("400x300")

    # Display account information

    info_labels = [

        f"Account ID: {account_info.login}",

        f"Balance: {account_info.balance}",

        f"Equity: {account_info.equity}",

        f"Margin: {account_info.margin}",

        f"Free Margin: {account_info.margin_free}",

        f"Leverage: {account_info.leverage}"

    ]

    for info in info_labels:

        label = tk.Label(info_window, text=info, font=("Helvetica", 14))

        label.pack(pady=5)

def fetch_and_display_chart(root):

    symbol = "USDMXN"

    timeframe = mt5.TIMEFRAME_D1

    days = 600  # Fetch 600 days of data

    data = fetch_historical_data(symbol, timeframe, days)

    scaled_data, scaler = preprocess_data(data)

    # Train the LSTM model

    lstm_model = train_lstm_model(scaled_data)

    

    # Train the Gradient Boosting model

    gb_model = train_gb_model(scaled_data)

    # Predict the future prices

    future_days = 60

    lstm_predictions = predict_future_lstm(lstm_model, scaled_data, future_days)

    gb_predictions = predict_future_gb(gb_model, scaled_data, future_days)

    

    # Combine predictions

    combined_predictions = (np.array(lstm_predictions) + np.array(gb_predictions)) / 2

    combined_predictions = scaler.inverse_transform(np.array(combined_predictions).reshape(-1, 1))

    # Append predicted prices to the original data

    predicted_dates = pd.date_range(start=data['time'].iloc[-1], periods=future_days + 1, closed='right')

    predicted_df = pd.DataFrame({'time': predicted_dates, 'close': combined_predictions.flatten()[:future_days]})

    # Plot and display the chart with peaks and predictions

    plot_predictions(data, predicted_df, root)

def fetch_historical_data(symbol, timeframe, days):

    rates = mt5.copy_rates_from_pos(symbol, timeframe, 0, days)

    if rates is None or len(rates) == 0:

        raise Exception("Failed to retrieve rates")

    df = pd.DataFrame(rates)

    df['time'] = pd.to_datetime(df['time'], unit='s')

    return df[['time', 'close']]

def preprocess_data(data):

    scaler = MinMaxScaler(feature_range=(0, 1))

    scaled_data = scaler.fit_transform(data['close'].values.reshape(-1, 1))

    return scaled_data, scaler

def train_lstm_model(scaled_data):

    time_step = 60

    X_train, y_train = create_train_data(scaled_data, time_step)

    model = Sequential()

    model.add(LSTM(100, return_sequences=True, input_shape=(X_train.shape[1], 1)))

    model.add(LSTM(100, return_sequences=False))

    model.add(Dense(50))

    model.add(Dense(1))

    model.compile(optimizer='adam', loss='mean_squared_error')

    model.fit(X_train, y_train, batch_size=32, epochs=50)  # Increased epochs for better training

    return model

def train_gb_model(scaled_data):

    time_step = 60

    X_train, y_train = create_train_data(scaled_data, time_step)

    model = GradientBoostingRegressor(n_estimators=100, learning_rate=0.01, max_depth=5)

    model.fit(X_train.reshape(X_train.shape[0], -1), y_train)

    return model

def create_train_data(scaled_data, time_step):

    X_train, y_train = [], []

    for i in range(time_step, len(scaled_data)):

        X_train.append(scaled_data[i-time_step:i, 0])

        y_train.append(scaled_data[i, 0])

    X_train, y_train = np.array(X_train), np.array(y_train)

    X_train = np.reshape(X_train, (X_train.shape[0], X_train.shape[1], 1))

    return X_train, y_train

def predict_future_lstm(model, data, future_days):

    predictions = []

    time_step = 60

    input_seq = data[-time_step:]

    for _ in range(future_days):

        input_seq = input_seq.reshape((1, input_seq.shape[0], 1))

        predicted_price = model.predict(input_seq)[0]

        predictions.append(predicted_price)

        input_seq = np.append(input_seq[:, 1:], predicted_price)

    return predictions

def predict_future_gb(model, data, future_days):

    predictions = []

    time_step = 60

    input_seq = data[-time_step:].reshape((1, -1))

    for _ in range(future_days):

        predicted_price = model.predict(input_seq)[0]

        predictions.append(predicted_price)

        input_seq = np.append(input_seq[:, 1:], predicted_price).reshape((1, -1))

    return predictions

def plot_predictions(data, predicted_df, root):

    combined_df = pd.concat([data, predicted_df])

    fig, ax = plt.subplots()

    ax.plot(combined_df['time'],